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segformer-b0-miic-tl

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metadata
license: other
base_model: nvidia/mit-b0
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b0-miic-tl
    results: []

segformer-b0-miic-tl

This model is a fine-tuned version of nvidia/mit-b0 on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4495
  • Mean Iou: 0.4202
  • Mean Accuracy: 0.8404
  • Overall Accuracy: 0.8404
  • Accuracy Unlabeled: nan
  • Accuracy Circuit: 0.8404
  • Iou Unlabeled: 0.0
  • Iou Circuit: 0.8404

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Circuit Iou Unlabeled Iou Circuit
0.465 1.0 20 0.6113 0.3296 0.6593 0.6593 nan 0.6593 0.0 0.6593
0.4071 2.0 40 0.4866 0.3345 0.6689 0.6689 nan 0.6689 0.0 0.6689
0.4429 3.0 60 0.3963 0.3623 0.7245 0.7245 nan 0.7245 0.0 0.7245
0.2408 4.0 80 0.3606 0.4257 0.8515 0.8515 nan 0.8515 0.0 0.8515
0.2002 5.0 100 0.3594 0.4345 0.8690 0.8690 nan 0.8690 0.0 0.8690
0.1885 6.0 120 0.3702 0.4291 0.8583 0.8583 nan 0.8583 0.0 0.8583
0.4626 7.0 140 0.3858 0.4128 0.8256 0.8256 nan 0.8256 0.0 0.8256
0.0865 8.0 160 0.3578 0.4456 0.8912 0.8912 nan 0.8912 0.0 0.8912
0.0752 9.0 180 0.3595 0.4387 0.8774 0.8774 nan 0.8774 0.0 0.8774
0.2567 10.0 200 0.4103 0.3981 0.7961 0.7961 nan 0.7961 0.0 0.7961
0.1419 11.0 220 0.4053 0.4229 0.8458 0.8458 nan 0.8458 0.0 0.8458
0.0623 12.0 240 0.3798 0.4415 0.8830 0.8830 nan 0.8830 0.0 0.8830
0.3336 13.0 260 0.3855 0.4374 0.8748 0.8748 nan 0.8748 0.0 0.8748
0.1283 14.0 280 0.3931 0.4368 0.8736 0.8736 nan 0.8736 0.0 0.8736
0.5155 15.0 300 0.4108 0.4268 0.8535 0.8535 nan 0.8535 0.0 0.8535
1.2662 16.0 320 0.4062 0.4328 0.8656 0.8656 nan 0.8656 0.0 0.8656
0.2631 17.0 340 0.3825 0.4464 0.8929 0.8929 nan 0.8929 0.0 0.8929
0.1751 18.0 360 0.3981 0.4335 0.8669 0.8669 nan 0.8669 0.0 0.8669
0.243 19.0 380 0.3963 0.4436 0.8872 0.8872 nan 0.8872 0.0 0.8872
0.1779 20.0 400 0.4413 0.4060 0.8119 0.8119 nan 0.8119 0.0 0.8119
0.0682 21.0 420 0.4106 0.4363 0.8725 0.8725 nan 0.8725 0.0 0.8725
0.2943 22.0 440 0.4052 0.4386 0.8771 0.8771 nan 0.8771 0.0 0.8771
0.118 23.0 460 0.4260 0.4197 0.8394 0.8394 nan 0.8394 0.0 0.8394
0.0865 24.0 480 0.4023 0.4270 0.8540 0.8540 nan 0.8540 0.0 0.8540
0.1693 25.0 500 0.4276 0.4199 0.8399 0.8399 nan 0.8399 0.0 0.8399
0.1778 26.0 520 0.4044 0.4409 0.8818 0.8818 nan 0.8818 0.0 0.8818
0.3617 27.0 540 0.4405 0.4121 0.8242 0.8242 nan 0.8242 0.0 0.8242
0.1688 28.0 560 0.4333 0.4234 0.8467 0.8467 nan 0.8467 0.0 0.8467
0.282 29.0 580 0.4060 0.4365 0.8730 0.8730 nan 0.8730 0.0 0.8730
0.0992 30.0 600 0.4297 0.4196 0.8393 0.8393 nan 0.8393 0.0 0.8393
1.379 31.0 620 0.4389 0.4193 0.8386 0.8386 nan 0.8386 0.0 0.8386
0.1355 32.0 640 0.4438 0.4205 0.8410 0.8410 nan 0.8410 0.0 0.8410
0.1067 33.0 660 0.4271 0.4299 0.8598 0.8598 nan 0.8598 0.0 0.8598
1.0659 34.0 680 0.4490 0.4063 0.8125 0.8125 nan 0.8125 0.0 0.8125
0.1481 35.0 700 0.4317 0.4279 0.8557 0.8557 nan 0.8557 0.0 0.8557
1.385 36.0 720 0.4215 0.4322 0.8644 0.8644 nan 0.8644 0.0 0.8644
0.3081 37.0 740 0.4564 0.4089 0.8178 0.8178 nan 0.8178 0.0 0.8178
0.1989 38.0 760 0.4345 0.4241 0.8482 0.8482 nan 0.8482 0.0 0.8482
0.1752 39.0 780 0.4230 0.4302 0.8605 0.8605 nan 0.8605 0.0 0.8605
0.1489 40.0 800 0.4253 0.4231 0.8462 0.8462 nan 0.8462 0.0 0.8462
0.1769 41.0 820 0.4184 0.4275 0.8549 0.8549 nan 0.8549 0.0 0.8549
0.1927 42.0 840 0.4162 0.4314 0.8629 0.8629 nan 0.8629 0.0 0.8629
0.2442 43.0 860 0.4321 0.4234 0.8468 0.8468 nan 0.8468 0.0 0.8468
0.2513 44.0 880 0.4280 0.4258 0.8515 0.8515 nan 0.8515 0.0 0.8515
0.7221 45.0 900 0.4449 0.4190 0.8380 0.8380 nan 0.8380 0.0 0.8380
0.0675 46.0 920 0.4369 0.4210 0.8419 0.8419 nan 0.8419 0.0 0.8419
0.1256 47.0 940 0.4159 0.4313 0.8625 0.8625 nan 0.8625 0.0 0.8625
0.1251 48.0 960 0.4312 0.4249 0.8498 0.8498 nan 0.8498 0.0 0.8498
0.2183 49.0 980 0.4340 0.4262 0.8524 0.8524 nan 0.8524 0.0 0.8524
0.2148 50.0 1000 0.4495 0.4202 0.8404 0.8404 nan 0.8404 0.0 0.8404

Framework versions

  • Transformers 4.36.2
  • Pytorch 1.11.0+cu115
  • Datasets 2.15.0
  • Tokenizers 0.15.0